{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "# Estimating the probability of future customer engagement\n",
    "\n",
    "\n",
    "A method that can be used to analyze customer value is RFM:\n",
    "* **R**ecency: how recently did the customer purchase?\n",
    "* **F**requency: how often do they purchase?\n",
    "* **M**onetary Value: how much do they spend?\n",
    "\n",
    "The [lifetimes](https://github.com/CamDavidsonPilon/lifetimes) library, that will be used here to analyze our customers, is based on a few assumptions according to its author:\n",
    "*  users interact with you when they are \"alive\"\n",
    "*  users under study may \"die\" after some period of time\n",
    "\n",
    "Considering this, `lifetimes` can be used to predict customers individuals who have churned from the service using only their usage history and we can define our own definition of \"alive\" or \"die\":\n",
    "* live: logins registered by online sessions\n",
    "* die: removed the service \n",
    "\n",
    "This library is based on the work developed by [Peter Fader and others](https://faculty.wharton.upenn.edu/wp-content/uploads/2012/06/BGNBD_FHL_MktgSci_2005_1.pdf) and the model is called Beta Geometric/Negative Binomial Distribution model (BG/NBD). It's interesting to note that this model allows for customers with no repeated transactions.\n",
    "\n",
    "## Definitions\n",
    "\n",
    "To build our model we need to obtain the following metrics:\n",
    "* **frequency**:  \n",
    "  * the number of dates on which a customer made a purchase subsequent to the date of the customer's first purchase\n",
    "* **T**(age):\n",
    "  * the number of time units (e.g. weeks, days, etc) since the date of a customer's first purchase to the current date (or last date in the dataset)\n",
    "* **recency**:\n",
    "  * the age(T) of the customer at the time of their last purchase\n",
    "  **monetary_value**:\n",
    "  * is the average value of a given customer’s purchases. This is equal to the sum of all a customer’s purchases divided by the total number of purchases.\n",
    "\n",
    "For more details, please see the original [documentation](https://lifetimes.readthedocs.io/en/latest/index.html).\n",
    "\n",
    "Moreover, to be able to use the library `lifetimes` and its functionalities we should put our data in the required format. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# importing libraries\n",
    "import datetime as dt\n",
    "from joblib import dump\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "# importing lifetimes\n",
    "import lifetimes\n",
    "from lifetimes import BetaGeoFitter, GammaGammaFitter\n",
    "from lifetimes.datasets import load_transaction_data\n",
    "from lifetimes.plotting import (\n",
    "    plot_calibration_purchases_vs_holdout_purchases,\n",
    "    plot_frequency_recency_matrix,\n",
    "    plot_history_alive,\n",
    "    plot_period_transactions,\n",
    "    plot_probability_alive_matrix,\n",
    ")\n",
    "from lifetimes.utils import (\n",
    "    calibration_and_holdout_data,\n",
    "    summary_data_from_transaction_data,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "lifetimes version: 0.11.3\n"
     ]
    }
   ],
   "source": [
    "# lifetimes library version\n",
    "print(f\"lifetimes version: {lifetimes.__version__}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Importing data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# reading the dataset\n",
    "#churn = pd.read_csv(\"../data/df_from_eng.csv\")\n",
    "churn = pd.read_csv(\"../data/df_from_casino_classic.csv\")\n",
    "\n",
    "# convert object column to datetime format\n",
    "churn[\"date\"] = pd.to_datetime(churn[\"date\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Formating and exploring data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>frequency</th>\n",
       "      <th>recency</th>\n",
       "      <th>T</th>\n",
       "      <th>monetary_value</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>player_id_cat</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>23.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>3456.782609</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>17.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>1126.647059</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               frequency  recency     T  monetary_value\n",
       "player_id_cat                                          \n",
       "1                   23.0     46.0  48.0     3456.782609\n",
       "2                   17.0     45.0  46.0     1126.647059\n",
       "4                    0.0      0.0  39.0        0.000000\n",
       "8                    0.0      0.0  14.0        0.000000"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# show a summary data from a pandas DataFrame that contains the customer_id col and the datetime col.\n",
    "\n",
    "# set time format: D=day, M=month, W=week\n",
    "freq = \"W\"\n",
    "\n",
    "observation_period_end = churn[\"date\"].max()  # the last transaction date\n",
    "\n",
    "rfm_metrics = summary_data_from_transaction_data(\n",
    "    transactions=churn,\n",
    "    customer_id_col=\"player_id_cat\",\n",
    "    datetime_col=\"date\",\n",
    "    monetary_value_col=\"session_sum\",\n",
    "    observation_period_end=observation_period_end,\n",
    "    freq=freq,\n",
    ")\n",
    "rfm_metrics.head(4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the above results, we can observe that, for example, customer with `player_id_cat = 4` :\n",
    "* used the service only once (zero frequency and zero recency)\n",
    "* the duration time between his first purchase and the end of the analysis period is 336 days"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of customers in the dataset: 6808\n"
     ]
    }
   ],
   "source": [
    "# numbers of custumers in the dataset\n",
    "print(f'Number of customers in the dataset: {churn[\"player_id_cat\"].nunique()}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         frequency      recency            T  monetary_value\n",
      "count  6808.000000  6808.000000  6808.000000     6808.000000\n",
      "mean      3.948884     9.535693    24.698149     6957.785714\n",
      "std       7.259045    13.456596    15.674937    13814.660040\n",
      "min       0.000000     0.000000     0.000000        0.000000\n",
      "25%       0.000000     0.000000    10.000000        0.000000\n",
      "50%       1.000000     2.000000    24.000000     2281.000000\n",
      "75%       4.000000    16.000000    39.000000     7179.375000\n",
      "max      51.000000    52.000000    52.000000   190704.000000\n",
      "\n",
      ">>> 40.8% of customers used the service only once\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# print some sdescriptive statistics\n",
    "# rfm = recency, frequency, monetary value\n",
    "print(f\"{rfm_metrics.describe()}\")\n",
    "print(\n",
    "    f'\\n>>> {round(sum(rfm_metrics[\"frequency\"] == 0) / float(len(rfm_metrics))*100, 1)}% of customers used the service only once\\n'\n",
    ")\n",
    "# plot frequency\n",
    "rfm_metrics[\"frequency\"].plot(kind=\"hist\", bins=50, **{\"figsize\": (12, 8)})\n",
    "plt.show()\n",
    "plt.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For our 9027 customers, in a 52 weeks period, the above results show that:\n",
    "* about 40.8% of the customers only used the service once"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Splitting the data\n",
    "\n",
    "We'll split the dataset into calibration (ealier in time) and holdout (later in time) periods. This is necessary for model validation and also to prevent overfitting.  \n",
    "\n",
    "The holdout period will be equal to 110 days, which approximately corresponds to 30% of the total time interval in the dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>frequency_cal</th>\n",
       "      <th>recency_cal</th>\n",
       "      <th>T_cal</th>\n",
       "      <th>frequency_holdout</th>\n",
       "      <th>duration_holdout</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>player_id_cat</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>14.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>16.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>16.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>16.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>16.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>16.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               frequency_cal  recency_cal  T_cal  frequency_holdout  \\\n",
       "player_id_cat                                                         \n",
       "1                       14.0         32.0   32.0                9.0   \n",
       "2                        8.0         10.0   30.0                9.0   \n",
       "4                        0.0          0.0   23.0                0.0   \n",
       "9                        0.0          0.0    2.0                2.0   \n",
       "12                       1.0          1.0   32.0                0.0   \n",
       "\n",
       "               duration_holdout  \n",
       "player_id_cat                    \n",
       "1                          16.0  \n",
       "2                          16.0  \n",
       "4                          16.0  \n",
       "9                          16.0  \n",
       "12                         16.0  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# split the data into calibration and holdout\n",
    "holdout_days = 110  # 30% of the time period in days\n",
    "calibration__period_end = observation_period_end - dt.timedelta(days=holdout_days)\n",
    "\n",
    "rfm_metrics_cal_holdout = calibration_and_holdout_data(\n",
    "    churn,\n",
    "    \"player_id_cat\",\n",
    "    \"date\",\n",
    "    calibration_period_end=calibration__period_end,\n",
    "    observation_period_end=observation_period_end,\n",
    "    freq=freq,\n",
    ")\n",
    "\n",
    "rfm_metrics_cal_holdout.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model building\n",
    "\n",
    "\n",
    "Let's use the split dataset, first fitting the model using the calibration and then evaluating the model using the holdout."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<lifetimes.BetaGeoFitter: fitted with 4402 subjects, a: 0.47, alpha: 1.56, b: 3.34, r: 0.43>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# fit the model\n",
    "model_bgf = BetaGeoFitter(penalizer_coef=0.0)\n",
    "model_bgf.fit(\n",
    "    rfm_metrics_cal_holdout[\"frequency_cal\"],\n",
    "    rfm_metrics_cal_holdout[\"recency_cal\"],\n",
    "    rfm_metrics_cal_holdout[\"T_cal\"],\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "r        0.427373\n",
       "alpha    1.556152\n",
       "a        0.467786\n",
       "b        3.343301\n",
       "Name: coef, dtype: float64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# show model parameters (r, alpha, a, b)\n",
    "model_bgf.summary.coef"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After fitting the model we are able to make some predictions considering the holdout period."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# predicted frequency during holdout period\n",
    "pred_freq_holdout = model_bgf.predict(\n",
    "    rfm_metrics_cal_holdout[\"duration_holdout\"],\n",
    "    rfm_metrics_cal_holdout[\"frequency_cal\"],\n",
    "    rfm_metrics_cal_holdout[\"recency_cal\"],\n",
    "    rfm_metrics_cal_holdout[\"T_cal\"],\n",
    ")\n",
    "\n",
    "# true frequency during holdout period\n",
    "true_freq_holdout = rfm_metrics_cal_holdout[\"frequency_holdout\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def model_eval(true, predicted, metric=\"mse\"):\n",
    "    # mean squared error and root mean squared error\n",
    "    if metric == \"mse\" or metric == \"rmse\":\n",
    "        val = np.sum(np.square(true - predicted)) / true.shape[0]\n",
    "        if metric == \"rmse\":\n",
    "            val = np.sqrt(val)\n",
    "            return print(f\"RMSE: {val}\")\n",
    "    # mean absolute error\n",
    "    elif metric == \"mae\":\n",
    "        val = np.sum(np.abs(true - predicted)) / true.shape[0]\n",
    "        return print(f\"MAE: {val}\")\n",
    "    else:\n",
    "        val = None\n",
    "    return print(f\"MSE: {val}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model evaluation\n",
    "\n",
    "Once we have the true and predicted values we can now evaluate our model. A simple way to do this is calculating the mean squared error, root mean squared error or mean absolute error implemented in the user-defined function `model_eval`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MSE: 7.38643018227433\n",
      "RMSE: 2.717798775162416\n",
      "MAE: 1.6235575569376972\n"
     ]
    }
   ],
   "source": [
    "# evaluate the model\n",
    "model_eval(true_freq_holdout, pred_freq_holdout, \"mse\")\n",
    "model_eval(true_freq_holdout, pred_freq_holdout, \"rmse\")\n",
    "model_eval(true_freq_holdout, pred_freq_holdout, \"mae\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A better approach to this is to compare purchase frequencies in the calibration period to relates to actual (frequency_holdout) and predicted (model_predictions) frequencies in the holdout period:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_calibration_purchases_vs_holdout_purchases(\n",
    "    model_bgf,\n",
    "    rfm_metrics_cal_holdout,\n",
    "    kind=\"frequency_cal\",\n",
    "    n=36,\n",
    "    **{\"figsize\": (12, 8)}\n",
    ")\n",
    "plt.show()\n",
    "plt.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the previous plot, we see that the model was able to capture the general trends in customer behavior."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_calibration_purchases_vs_holdout_purchases(\n",
    "    model_bgf,\n",
    "    rfm_metrics_cal_holdout,\n",
    "    kind=\"time_since_last_purchase\",\n",
    "    n=36,\n",
    "    **{\"figsize\": (12, 8)}\n",
    ")\n",
    "plt.show()\n",
    "plt.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The previous plot shows the time since-user made last purchase (in weeks) relative to the average number of purchases in the holdout. When the time since last purchase increase, the number of purchases in the holdout decrease, which means that customer who does not use the service for some time, probably will not come back.\n",
    "\n",
    "Let's now calculate the probability a consumer is currently \"alive\"."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>frequency</th>\n",
       "      <th>recency</th>\n",
       "      <th>T</th>\n",
       "      <th>monetary_value</th>\n",
       "      <th>prob_alive</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>player_id_cat</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>23.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>3456.782609</td>\n",
       "      <td>0.953786</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>17.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>1126.647059</td>\n",
       "      <td>0.966161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>9546.500000</td>\n",
       "      <td>0.620136</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               frequency  recency     T  monetary_value  prob_alive\n",
       "player_id_cat                                                      \n",
       "1                   23.0     46.0  48.0     3456.782609    0.953786\n",
       "2                   17.0     45.0  46.0     1126.647059    0.966161\n",
       "4                    0.0      0.0  39.0        0.000000    1.000000\n",
       "8                    0.0      0.0  14.0        0.000000    1.000000\n",
       "9                    2.0      8.0  18.0     9546.500000    0.620136"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# create a field with the probability a customer is currently \"alive\"\n",
    "\n",
    "rfm_metrics[\"prob_alive\"] = model_bgf.conditional_probability_alive(\n",
    "    rfm_metrics[\"frequency\"], \n",
    "    rfm_metrics[\"recency\"], \n",
    "    rfm_metrics[\"T\"]\n",
    ")\n",
    "\n",
    "rfm_metrics.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can inspect how the probability of being alive changes over time. For this, we can choose one customer from our dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# probability of staying alive\n",
    "plt.figure(figsize=(12, 8))\n",
    "custumer = 2\n",
    "days_since_birth = 52\n",
    "trans_hist = churn.loc[churn[\"player_id_cat\"] == custumer]\n",
    "plot_history_alive(\n",
    "    model_bgf,\n",
    "    days_since_birth,\n",
    "    trans_hist,\n",
    "    \"date\",\n",
    "    freq=freq,\n",
    ")\n",
    "plt.show()\n",
    "plt.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see how the probability changes when the customer re-engages to the service.\n",
    "\n",
    "The first purchase for this consumer was in January 2013. Five months of activity were followed by 4 months of inactivity when the probability was drastically reduced, and then the consumer re-engaged again.  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also plot the probability of being alive as a heatmap relative to frequency and recency."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# probability of being alive by frequency\n",
    "plt.subplots(figsize=(12, 8))\n",
    "plot_probability_alive_matrix(model_bgf, max_frequency=200)\n",
    "plt.show()\n",
    "plt.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Customers who have purchased lately are more likely to be “alive”.\n",
    "\n",
    "We can also visualize  the number of purchases expected from a customer over a given future time interval, such as over the next 4 weeks."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# future expected purchases\n",
    "plt.subplots(figsize=(12, 8))\n",
    "\n",
    "plot_frequency_recency_matrix(model_bgf, T=4, max_frequency=200)\n",
    "plt.show()\n",
    "plt.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the previous plot, on the bottom-right corner are the customers who purchase more and recently and in the top-left corner are those who purchase a lot but not recently, and are prone to disengage. \n",
    "\n",
    "In general, in the last two plots, the best customers appear on the bottom-right corner, and the most likely to disengage appear on the top-left corner.\n",
    "\n",
    "We can also calculate the probability of purchases expected from a customer over a given future time interval. Four weeks in our case."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>frequency</th>\n",
       "      <th>recency</th>\n",
       "      <th>T</th>\n",
       "      <th>monetary_value</th>\n",
       "      <th>prob_alive</th>\n",
       "      <th>purchase_next_4_weeks</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>player_id_cat</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>23.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>3456.782609</td>\n",
       "      <td>0.953786</td>\n",
       "      <td>1.773636</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>17.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>1126.647059</td>\n",
       "      <td>0.966161</td>\n",
       "      <td>1.392427</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.041796</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.107560</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>9546.500000</td>\n",
       "      <td>0.620136</td>\n",
       "      <td>0.299726</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               frequency  recency     T  monetary_value  prob_alive  \\\n",
       "player_id_cat                                                         \n",
       "1                   23.0     46.0  48.0     3456.782609    0.953786   \n",
       "2                   17.0     45.0  46.0     1126.647059    0.966161   \n",
       "4                    0.0      0.0  39.0        0.000000    1.000000   \n",
       "8                    0.0      0.0  14.0        0.000000    1.000000   \n",
       "9                    2.0      8.0  18.0     9546.500000    0.620136   \n",
       "\n",
       "               purchase_next_4_weeks  \n",
       "player_id_cat                         \n",
       "1                           1.773636  \n",
       "2                           1.392427  \n",
       "4                           0.041796  \n",
       "8                           0.107560  \n",
       "9                           0.299726  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rfm_metrics[\n",
    "    \"purchase_next_4_weeks\"\n",
    "] = model_bgf.conditional_expected_number_of_purchases_up_to_time(\n",
    "    4, rfm_metrics[\"frequency\"], rfm_metrics[\"recency\"], rfm_metrics[\"T\"]\n",
    ")\n",
    "\n",
    "rfm_metrics.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can use our model to predict the number of purchases expected from the customer over some number of weeks given a frequency, recency and an age (T) for the customer."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Probability of Alive: [0.73805765]\n",
      "Expected Purchases in next 4 weeks: 0.2411686139390064\n"
     ]
    }
   ],
   "source": [
    "# using our model to make predictions\n",
    "frequency = 4\n",
    "recency = 36\n",
    "T = 52\n",
    "t = 4\n",
    "\n",
    "being_alive = model_bgf.conditional_probability_alive(frequency, recency, T)\n",
    "expected_purchases = model_bgf.conditional_expected_number_of_purchases_up_to_time(t, frequency, recency, T)               \n",
    "\n",
    "print(    f\"Probability of Alive: {being_alive}\")\n",
    "print(    f\"Expected Purchases in next {4} weeks: {expected_purchases}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(6808, 6)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rfm_metrics.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Saving dataset and model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# save dataframe to file\n",
    "rfm_metrics.to_csv(\"../data/df_from_gb-nbd.csv\", index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# save the model\n",
    "model_bgf.save_model('../models/model_bg-nbd.pkl')"
   ]
  }
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